Datasets:
meta_woz

Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: crowdsourced
Annotations Creators: crowdsourced
Source Datasets: original
Licenses: other
meta_woz / meta_woz.py
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models"""
import json
import os
import datasets
_CITATION = """\
@InProceedings{shalyminov2020fast,
author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2020},
month = {April},
url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a
-hybrid-generative-retrieval-transformer/},
}
"""
_DESCRIPTION = """\
MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. \
We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for \
conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to \
quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas \
of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two \
human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human \
user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a \
particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. \
Dialogues are a minimum of 10 turns long.
"""
_HOMEPAGE = "https://www.microsoft.com/en-us/research/project/metalwoz/"
_LICENSE = "Microsoft Research Data License Agreement"
_URLs = {
"train": "https://download.microsoft.com/download/E/B/8/EB84CB1A-D57D-455F-B905-3ABDE80404E5/metalwoz-v1.zip",
"test": "https://download.microsoft.com/download/0/c/4/0c4a8893-cbf9-4a43-a44a-09bab9539234/metalwoz-test-v1.zip",
}
class MetaWoz(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="dialogues", description="The dataset of dialogues from various domains."),
datasets.BuilderConfig(
name="tasks", description="The metadata for tasks corresponding to dialogues from " "various domains."
),
]
DEFAULT_CONFIG_NAME = "dialogues"
def _info(self):
if self.config.name == "tasks":
features = datasets.Features(
{
"task_id": datasets.Value("string"),
"domain": datasets.Value("string"),
"bot_prompt": datasets.Value("string"),
"bot_role": datasets.Value("string"),
"user_prompt": datasets.Value("string"),
"user_role": datasets.Value("string"),
}
)
else:
features = datasets.Features(
{
"id": datasets.Value("string"),
"user_id": datasets.Value("string"),
"bot_id": datasets.Value("string"),
"domain": datasets.Value("string"),
"task_id": datasets.Value("string"),
"turns": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLs)
data_dir["test"] = dl_manager.extract(os.path.join(data_dir["test"], "dstc8_metalwoz_heldout.zip"))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"data_dir": data_dir["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"data_dir": data_dir["test"]},
),
]
def _generate_examples(self, data_dir):
"""Yields examples."""
if self.config.name == "tasks":
filepath = os.path.join(data_dir, "tasks.txt")
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, {
"task_id": data["task_id"],
"domain": data["domain"],
"bot_prompt": data["bot_prompt"],
"bot_role": data["bot_role"],
"user_prompt": data["user_prompt"],
"user_role": data["user_role"],
}
else:
id_ = -1
base_path = os.path.join(data_dir, "dialogues")
file_list = sorted(
[os.path.join(base_path, file) for file in os.listdir(base_path) if file.endswith(".txt")]
)
for filepath in file_list:
with open(filepath, encoding="utf-8") as f:
for row in f:
id_ += 1
data = json.loads(row)
yield id_, {
"id": data["id"],
"user_id": data["user_id"],
"bot_id": data["bot_id"],
"domain": data["domain"],
"task_id": data["task_id"],
"turns": data["turns"],
}